CCAM captures global contextual information in a similar fashion to the non-local module [1]} by generating an attention map for each pixel in the input feature map and then transforming it into a refined feature map. However, instead of generating attention maps for each pixel-pair which is computationally expensive, for each pixel in a feature map, CCAM aggregates contextual information only in its horizontal and vertical directions, as shown in Figure REF . By consecutively stacking two criss-cross attention modules, each pixel can collect contextual information from all pixels in a given feature map. Next, the contextually rich feature is concatenated and convolved with the original feature maps for feature fusion. Our experiments consisted of systematically adding the attention modules in two strategic locations in the network: first, in the backbone’s output features, next in the FPN output features, and ultimately on both locations. As a result, we came up with three models, CCAM-Backbone, CCAM-FPN, and CCAM-Full, plus the attentionless baseline Base YOLACT++.
